Despite the success of deep learning based models in medical image segmentation, most state-of-the-art (SOTA) methods perform fully-supervised learning, which commonly rely on large scale annotated training datasets. However, medical image annotation is highly time-consuming, hindering its clinical applications. Semi-supervised learning (SSL) has been emerged as an appealing strategy in training with limited annotations, largely reducing the labelling cost. We propose a novel SSL framework SSL-MedSAM2, which contains a training-free few-shot learning branch TFFS-MedSAM2 based on the pretrained large foundation model Segment Anything Model 2 (SAM2) for pseudo label generation, and an iterative fully-supervised learning branch FSL-nnUNet based on nnUNet for pseudo label refinement. The results on MICCAI2025 challenge CARE-LiSeg (Liver Segmentation) demonstrate an outstanding performance of SSL-MedSAM2 among other methods. The average dice scores on the test set in GED4 and T1 MRI are 0.9710 and 0.9648 respectively, and the Hausdorff distances are 20.07 and 21.97 respectively. The code is available via https://github.com/naisops/SSL-MedSAM2/tree/main.

翻译：尽管基于深度学习的模型在医学图像分割领域取得了成功，但大多数最先进（SOTA）方法采用全监督学习，通常依赖于大规模标注的训练数据集。然而，医学图像标注极其耗时，阻碍了其临床应用。半监督学习（SSL）已成为一种在有限标注下进行训练的有吸引力的策略，可大幅降低标注成本。我们提出了一种新颖的SSL框架SSL-MedSAM2，它包含一个基于预训练大型基础模型Segment Anything Model 2（SAM2）的免训练少样本学习分支TFFS-MedSAM2，用于生成伪标签；以及一个基于nnUNet的迭代全监督学习分支FSL-nnUNet，用于伪标签细化。在MICCAI2025挑战赛CARE-LiSeg（肝脏分割）上的结果表明，SSL-MedSAM2在其他方法中表现出色。在测试集上，GED4和T1 MRI的平均Dice分数分别为0.9710和0.9648，Hausdorff距离分别为20.07和21.97。代码可通过https://github.com/naisops/SSL-MedSAM2/tree/main获取。